Buildkite Pipeline Patterns#

Buildkite splits CI/CD into two parts: a hosted web service that manages pipelines, builds, and the UI, and self-hosted agents that execute the actual work. This architecture means your code, secrets, and build artifacts never touch Buildkite’s infrastructure. The agents run on your machines – EC2 instances, Kubernetes pods, bare metal, laptops.

Why Teams Choose Buildkite#

The question usually comes up against Jenkins and GitHub Actions.

Over Jenkins: Buildkite eliminates the Jenkins controller as a single point of failure. There is no plugin compatibility hell, no Groovy DSL, no Java memory tuning. Agents are stateless binaries that poll for work. Scaling is adding more agents. Jenkins requires careful capacity planning of the controller itself.

Agent Runbook Generation#

An agent that says “you should probably add a readiness probe to your deployment” is giving advice. An agent that hands you a tested manifest with the readiness probe configured, verified against a real cluster, with rollback steps if the probe misconfigures – that agent is producing a deliverable. The difference matters.

The core thesis of infrastructure agent work is that the output is always a deliverable – a runbook, playbook, tested manifest, or validated configuration – never a direct action on someone else’s systems. This article covers the complete workflow for generating those deliverables: understanding requirements, planning steps, executing in a sandbox, capturing what worked, and packaging the result.

The Manual-to-Automated Progression#

Not every runbook should be automated, and automation does not happen in a single jump. The progression builds confidence at each stage.

Level 0 – Tribal Knowledge: The procedure exists only in someone’s head. Invisible risk.

Level 1 – Documented Runbook: Step-by-step instructions a human follows, including commands, expected outputs, and decision points. Every runbook starts here.

Level 2 – Scripted Runbook: Manual steps encoded in a script that a human triggers and monitors. The script handles tedious parts; the human handles judgment calls.

Detecting Infrastructure Knowledge Gaps#

The most dangerous thing an agent can do is confidently produce a deliverable based on wrong assumptions. An agent that assumes x86_64 when the target is ARM64, that assumes PostgreSQL 14 behavior when the target runs 15, or that assumes AWS IAM patterns when the target is Azure – that agent produces a runbook that will fail in ways the human did not expect and may not understand.

Prompt Engineering for Infrastructure Operations#

Infrastructure prompts differ from general-purpose prompts in one critical way: the output often drives real actions on real systems. A hallucinated filename in a creative writing task is harmless. A hallucinated resource name in a Kubernetes delete command causes an outage. Every prompt pattern here is designed with that asymmetry in mind – prioritizing correctness and safety over cleverness.

Structured Output for Infrastructure Data#

Infrastructure operations produce structured data: IP addresses, resource names, status codes, configuration values. Free-form text responses create parsing fragility. Force structured output from the start.